{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 神经网络\n",
    "\n",
    "1. 逻辑回归回顾\n",
    "2. 参数更新原理\n",
    "3. softmax\n",
    "4. 极简神经网络\n",
    "5. Relu\n",
    "6. TensorFlow实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归回顾\n",
    "\n",
    "https://blog.csdn.net/Yellow_python/article/details/81240395\n",
    "\n",
    "$ \\theta^{t+1} = \\theta^t + \\alpha \\frac{1}{n} \\sum_{i=1}^n (y_i - h_{\\theta}(x_i))x_i $\n",
    "\n",
    "$ \\theta := \\theta + \\alpha X^T (y - h) $"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://img-blog.csdnimg.cn/2019010421374960.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_50,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1llbGxvd19weXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np, matplotlib.pyplot as mp\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "\"\"\"创建随机样本\"\"\"\n",
    "X, Y = make_blobs(centers=2, cluster_std=5)\n",
    "\n",
    "\"\"\"数据处理\"\"\"\n",
    "X = np.insert(X, 0, 1, axis=1)  # 增加一列，用于矩阵相乘\n",
    "Y = Y.reshape(-1, 1)\n",
    "\n",
    "\"\"\"sigmoid函数\"\"\"\n",
    "sigmoid = lambda x: 1 / (1 + np.exp(-x))\n",
    "\n",
    "\"\"\"梯度上升\"\"\"\n",
    "d = X.shape[1]  # 维数\n",
    "theta = np.mat([[1]] * d)  # 初始化回归系数\n",
    "for i in range(5999, 8999):\n",
    "    alpha = 1 / i  # 步长（先大后小）\n",
    "    h = sigmoid(X * theta)\n",
    "    theta = theta + alpha * X.T * (Y - h)  # 最终梯度上升迭代公式\n",
    "\n",
    "\"\"\"数据可视化\"\"\"\n",
    "x1, x2 = X[:, 1], X[:, 2]\n",
    "mp.axis([x1.min() + 1, x1.max() + 1, x2.min() + 1, x2.max() + 1])\n",
    "mp.scatter(x1, x2, c=Y.reshape(-1))  # 原始样本点\n",
    "x = np.array([x1.min(), x1.max()])\n",
    "y = (-theta[0, 0] - theta[1, 0] * x) / theta[2, 0]  # 决策边界\n",
    "mp.plot(x, y)\n",
    "mp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参数更新原理\n",
    "\n",
    "![](https://img-blog.csdnimg.cn/20190118212740618.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_40,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1llbGxvd19weXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## softmax\n",
    "或称`归一化指数函数`，是有限项离散概率分布的梯度对数归一化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://img-blog.csdnimg.cn/20190106182457458.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_40,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1llbGxvd19weXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极简神经网络\n",
    "https://blog.csdn.net/Yellow_python/article/details/80545909"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://img-blog.csdnimg.cn/20190109181519914.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1llbGxvd19weXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error:0.423575\n",
      "Error:0.007115\n",
      "Error:0.004951\n",
      "Error:0.004019\n",
      "Error:0.003470\n",
      "Error:0.003097\n",
      "Error:0.002823\n",
      "Error:0.002611\n",
      "Error:0.002440\n",
      "Error:0.002299\n",
      "Error:0.002180\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "\n",
    "def sigmoid(x, deriv=False):\n",
    "    if deriv:  # 导数\n",
    "        return x * (1 - x)  # S'(x) = S(x) (1 - S(x))\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "# 样本\n",
    "X = np.array([[0, 0, 1],\n",
    "              [0, 1, 1],\n",
    "              [1, 0, 1],\n",
    "              [1, 1, 1],\n",
    "              [0, 1, 0]])  # 5*3\n",
    "y = np.array([[0],\n",
    "              [1],\n",
    "              [1],\n",
    "              [1],\n",
    "              [0]])  # 5*1\n",
    "\n",
    "# 初始化权重系数（随机）\n",
    "n = 4  # 神经元个数\n",
    "d = X.shape[1]  # dimensionality\n",
    "w1 = np.random.random((d, n))  # Weight1\n",
    "w2 = np.random.random((n, 1))  # Weight2\n",
    "# 训练\n",
    "for j in range(99999):\n",
    "    l1 = sigmoid(np.dot(X, w1))  # Layer1\n",
    "    l2 = sigmoid(np.dot(l1, w2))  # Layer2\n",
    "\n",
    "    l2_error = y - l2\n",
    "    l2_delta = l2_error * sigmoid(l2, deriv=True)  # △Layer2\n",
    "    if j % 9999 == 0:\n",
    "        print('Error:%f' % np.mean(np.abs(l2_error)))\n",
    "\n",
    "    l1_error = l2_delta.dot(w2.T)\n",
    "    l1_delta = l1_error * sigmoid(l1, deriv=True)  # △Layer1\n",
    "\n",
    "    w2 += l1.T.dot(l2_delta)\n",
    "    w1 += X.T.dot(l1_delta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## relu\n",
    "\n",
    "线性整流函数（Rectified Linear Unit，ReLU），又称修正线性单元，是一种人工神经网络中常用的激活函数（activation function），通常指代以斜坡函数及其变种为代表的非线性函数。\n",
    "\n",
    "https://blog.csdn.net/Yellow_python/article/details/85769987"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://img-blog.csdnimg.cn/20190106173801484.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_70,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1llbGxvd19weXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率：0.99\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np, matplotlib.pyplot as mp\n",
    "\n",
    "\"\"\"创建随机样本\"\"\"\n",
    "def make_moons(n_samples=100, n_features=2, species=3):\n",
    "    X = np.zeros((n_samples * species, n_features))\n",
    "    y = np.zeros(n_samples * species, dtype=int)\n",
    "    for j in range(species):\n",
    "        ix = range(n_samples * j, n_samples * (j + 1))\n",
    "        r = np.linspace(0, 1, n_samples)\n",
    "        t = np.linspace(j * 4, (j + 1) * 4, n_samples) + np.random.randn(n_samples) * .2\n",
    "        X[ix] = np.c_[r * np.sin(t), r * np.cos(t)]\n",
    "        y[ix] = j\n",
    "    return X, y\n",
    "X, y = make_moons()\n",
    "\n",
    "n = X.shape[0]  # 样本数\n",
    "d = X.shape[1]  # 维数\n",
    "k = len(np.unique(y))  # 种类数\n",
    "\n",
    "\"\"\"神经网络模型参数\"\"\"\n",
    "nn = 100  # 神经元个数\n",
    "W1 = .01 * np.random.randn(d, nn)\n",
    "b1 = np.zeros((1, nn))\n",
    "W2 = .01 * np.random.randn(nn, k)\n",
    "b2 = np.zeros((1, k))\n",
    "\n",
    "\"\"\"梯度下降循环\"\"\"\n",
    "for i in range(2999):\n",
    "    # 1、ReLU激活\n",
    "    hidden_layer = np.maximum(0, np.dot(X, W1) + b1)\n",
    "    # 2、得分\n",
    "    scores = np.dot(hidden_layer, W2) + b2\n",
    "    # 3、概率\n",
    "    exp_scores = np.exp(scores)\n",
    "    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
    "    # 4、梯度\n",
    "    probs[range(n), y] -= 1\n",
    "    dscores = probs / n\n",
    "    # 5、反向传播：ReLU\n",
    "    dhidden = np.dot(dscores, W2.T)\n",
    "    dhidden[hidden_layer <= 0] = 0\n",
    "    # 6、参数更新：W2、b2\n",
    "    W2 -= np.dot(hidden_layer.T, dscores)\n",
    "    b2 -= np.sum(dscores, axis=0, keepdims=True)\n",
    "    # 7、参数更新：W1、b1\n",
    "    W1 -= np.dot(X.T, dhidden)\n",
    "    b1 -= np.sum(dhidden, axis=0, keepdims=True)\n",
    "\n",
    "\"\"\"模型评估\"\"\"\n",
    "hidden_layer = np.maximum(0, np.dot(X, W1) + b1)\n",
    "scores = np.dot(hidden_layer, W2) + b2\n",
    "predicted_class = np.argmax(scores, axis=1)\n",
    "print('准确率：%.2f' % (np.mean(predicted_class == y)))\n",
    "\n",
    "\"\"\"可视化\"\"\"\n",
    "x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
    "y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))\n",
    "Z = np.dot(np.maximum(0, np.dot(np.c_[xx.ravel(), yy.ravel()], W1) + b1), W2) + b2\n",
    "Z = np.argmax(Z, axis=1)\n",
    "Z = Z.reshape(xx.shape)\n",
    "mp.contourf(xx, yy, Z, alpha=.1)  # 填色等位线\n",
    "mp.scatter(X[:, 0], X[:, 1], c=y, s=20, alpha=.7)\n",
    "mp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow\n",
    "https://blog.csdn.net/Yellow_python/article/details/89108961#_107"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "\"\"\"配置\"\"\"\n",
    "train_dir = 'data/'  # 数据集路径\n",
    "units = 256  # 神经元数量\n",
    "\n",
    "\"\"\"加载样本集：手写数字\"\"\"\n",
    "mnist = input_data.read_data_sets(train_dir, one_hot=True)\n",
    "\n",
    "\"\"\"多层感知机\"\"\"\n",
    "# 输入层\n",
    "X = tf.placeholder('float', [None, 28 * 28])\n",
    "Y = tf.placeholder('float', [None, 10])  # 10分类（0~9）\n",
    "\n",
    "# 全连接层1\n",
    "W1 = tf.Variable(tf.truncated_normal([28 * 28, units], stddev=.1))\n",
    "b1 = tf.Variable(tf.constant(.1, shape=[units]))\n",
    "h1 = tf.nn.relu(tf.matmul(X, W1) + b1)\n",
    "\n",
    "# 全连接层2\n",
    "W2 = tf.Variable(tf.truncated_normal([units, 10], stddev=.1))\n",
    "b2 = tf.Variable(tf.constant(.1, shape=[10]))\n",
    "h2 = tf.matmul(h1, W2) + b2\n",
    "\n",
    "# softmax交叉熵损失\n",
    "loss = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=h2)\n",
    "\n",
    "# Adam优化器\n",
    "optimizer = tf.train.AdamOptimizer().minimize(loss)\n",
    "\n",
    "\"\"\"精度\"\"\"\n",
    "prediction = tf.equal(tf.argmax(h2, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(prediction, 'float'))\n",
    "\n",
    "\"\"\"运行\"\"\"\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "batch_size = 50\n",
    "for i in range(501):\n",
    "    inputs, labels = mnist.train.next_batch(batch_size)  # 取一批\n",
    "    optimizer.run(session=sess, feed_dict={X: inputs, Y: labels})  # 训练\n",
    "    if i % 10 == 0:\n",
    "        train_accuracy = accuracy.eval(session=sess, feed_dict={X: inputs, Y: labels})\n",
    "        print('step %d, accuracy %g' % (i, train_accuracy))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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